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MDM/KDD

Osmar R. Zaïane, Simeon J. Simoff
2002 SIGKDD Explorations  
This is brief report summarizes the presentations, conclusions and directions for future work that were discussed during the second edition of the International Workshop on Multimedia Data Mining.  ...  Acknowledgements We are thankful to the members of the KDD workshop committee for providing the opportunity to stage this event.  ...  Special thanks go to the workshop program committee for promptly providing the organizers with high quality paper reviews.  ... 
doi:10.1145/507515.507524 fatcat:lhbmmnnscrhwjplewnhniee2tu

A Review of Denoising Medical Images Using Machine Learning Approaches

Prabhpreet Kaur, Gurvinder Singh, Parminder Kaur
2018 Current Medical Imaging Reviews  
This paper attempts to identify suitable Machine Learning (ML) approach for image denoising of radiology based medical application.  ...  In most of the applications, the machine learning performance is better than the conventional image denoising techniques.  ...  Title: "Hybrid Approach for automatic segmentation of fetal abdomen from ultrasound images using deep learning" [19] Author "HOG feature outperform Haar Features by more than 4%."  ... 
doi:10.2174/1573405613666170428154156 pmid:30532667 pmcid:PMC6225344 fatcat:tyfmwr7dszh2paqx7m4ktue4oi

Mining Signatures from Event Sequences

Rajput S.H., Chetan Jadhav, Yogesh Deshmukh, Sandip Sonawane, Hemant Jadhav
2015 IJARCCE  
This paper proposes a novel secular knowledge representation and learning framework to proposed largescale secular signature mining of longitudinal heterogeneous occasional data.  ...  The prescribed data representation maps the heterogeneous sequences to a image by encoding occasions as a structured spatial-secular shape process.  ...  Naive Bayes: The Bayesian Classification shows a supervised learning technique as well as a statistical method for classification.  ... 
doi:10.17148/ijarcce.2015.44129 fatcat:up6onqghvje7rgj6bi4o4aw7ky

A survey on computational intelligence approaches for predictive modeling in prostate cancer

Georgina Cosma, David Brown, Matthew Archer, Masood Khan, A. Graham Pockley
2017 Expert systems with applications  
), Artificial Neural Networks, Deep Learning, Fuzzy based approaches, and hybrids of these, as well as Bayesian based approaches, and Markov models.  ...  In particular, the paper considers a broad definition of computational intelligence which includes evolutionary algorithms (also known as metaheuristic optimisation, nature inspired optimisation algorithms  ...  Decision 805 trees are a popular method for various machine learning tasks. Decision trees can grow to be very deep in order to be capable of learning irregular patterns.  ... 
doi:10.1016/j.eswa.2016.11.006 fatcat:ii6gbq6qcbai5kxvcy4l7kkg54

A review on automatic image annotation techniques

Dengsheng Zhang, Md. Monirul Islam, Guojun Lu
2012 Pattern Recognition  
The typical method of bridging the semantic gap is through the automatic image annotation (AIA) which extracts semantic features using machine learning techniques.  ...  Nowadays, more and more images are available. However, to find a required image for an ordinary user is a challenging task.  ...  Acknowledgement The authors are grateful for the constructive and valuable comments made by the many expert reviewers.  ... 
doi:10.1016/j.patcog.2011.05.013 fatcat:tse6w7ltwnactpra3kgplc3yqi

3D Reconstruction of Underground Tunnel Using Depth-camera-based Inspection Robot

Ningbo Jing, Xianmin Ma, Wei Guo, Mei Wang
2019 Sensors and materials  
The feature points of a depth image are extracted to realize the precise matching between the RGB and depth images.  ...  Establishing a 3D model of an underground environment for an inspection robot has received significant attention and concern in recent years. RGB and depth images are obtained using a depth camera.  ...  of Xi'an University of Science and Technology (201213), and the Natural Science Special Project of Shaanxi Province (14JK1467).  ... 
doi:10.18494/sam.2019.2321 fatcat:mgvgeushibflnbxla2knmx3zlu

2020 Index IEEE Journal of Biomedical and Health Informatics Vol. 24

2020 IEEE journal of biomedical and health informatics  
., A Globalized Model for Mapping Wearable Seismocardiogram Signals to Whole-Body Ballistocardiogram Signals Based on Deep Learning; JBHI May 2020 1296-1309 Herskovic, V., see Saint-Pierre, C., JBHI Jan  ...  , D. 2570-2579 Jiang, D., see 2473-2480 Jiang, H., see 2798-2805 Jiang, H., Yang, M., Chen, X., Li, M., Li, Y., and Wang, J., miRTMC: A miRNA Target Prediction Method Based on Matrix Completion Algorithm  ...  ., +, JBHI March 2020 844-854 Automatic Identification of Breast Ultrasound Image Based on Supervised Block-Based Region Segmentation Algorithm and Features Combination Migration Deep Learning Model.  ... 
doi:10.1109/jbhi.2020.3048808 fatcat:iifrkwtzazdmboabdqii7x5ukm

Systematic Literature Review on Data-Driven Models for Predictive Maintenance of Railway Track: Implications in Geotechnical Engineering

Jiawei Xie, Jinsong Huang, Cheng Zeng, Shui-Hua Jiang, Nathan Podlich
2020 Geosciences  
It is found that applying the deep learning methods, unsupervised methods, and ensemble methods are the new trends for predictive maintenance of railway track.  ...  Conventional planning of maintenance and renewal work for railway track is based on heuristics and simple scheduling.  ...  Unsupervised Learning Models Unsupervised learning aims to find patterns automatically from unlabeled data.  ... 
doi:10.3390/geosciences10110425 fatcat:r73zrv554vcsnjbi2aaeswvbsq

A Review of Artificial Intelligence Methods for Condition Monitoring and Fault Diagnosis of Rolling Element Bearings for Induction Motor

Omar AlShorman, Muhammad Irfan, Nordin Saad, D. Zhen, Noman Haider, Adam Glowacz, Ahmad AlShorman, Yongfang Zhang
2020 Shock and Vibration  
Moreover, these techniques include signal/image processing, intelligent diagnostics, data fusion, data mining, and expert systems for time and frequency as well as time-frequency domains.  ...  Rolling bearings are considered to be the main component of IM. Undoubtedly, any failure of this basic component can lead to a serious breakdown of IM and for whole industrial system.  ...  Currently, the Bayesian network is extensively used [171] in several applications, such as feature extraction and classification machine learning algorithms, data mining and data processing, speech  ... 
doi:10.1155/2020/8843759 fatcat:h4zyvhct6nb7lpsj7j5f3yror4

Statistical algorithms for ontology-based annotation of scientific literature

Chayan Chakrabarti, Thomas B Jones, George F Luger, Jiawei F Xu, Matthew D Turner, Angela R Laird, Jessica A Turner
2014 Journal of Biomedical Semantics  
Results: We compare our results across naïve Bayes, Bayesian Decision Trees, and Constrained Decision Tree classifiers that keep a human expert in the loop, in terms of the quality measure of the F1-mirco  ...  Methods: We present a probabilistic framework that facilitates the automatic annotation of literature by indirectly modeling the restrictions among the different classes in the ontology.  ...  Acknowledgements This project is made possible by a collaboration agreement allowing comprehensive access to the BrainMap database, a copyrighted electronic compilation owned by the University of Texas  ... 
doi:10.1186/2041-1480-5-s1-s2 pmid:25093071 pmcid:PMC4108869 fatcat:gbkxmhq2i5g37mnbh3gyguyck4

Predictive data mining in clinical medicine: a focus on selected methods and applications

Riccardo Bellazzi, Fulvia Ferrazzi, Lucia Sacchi
2011 Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery  
This review describes the main features of predictive clinical data mining and focus on two specific aspects of particular interest: the methods able to deal with temporal data and the efforts performed  ...  Predictive data mining in clinical medicine deals with learning models to predict patients' health. The models can be devoted to support clinicians in diagnostic, therapeutic, or monitoring tasks.  ...  Moreover, a variable selection tool is implicitly provided by the Bayesian network learning algorithm as, by the global Markov property, a node is conditionally independent of all other variables in the  ... 
doi:10.1002/widm.23 fatcat:n6juaunarbcclkfur3ojwb6zpq

A survey on Data Mining approaches for Healthcare

Divya Tomar, Sonali Agarwal
2013 International Journal of Bio-Science and Bio-Technology  
In this paper, we present a brief introduction of these techniques and their advantages and disadvantages.  ...  Data Mining is one of the most motivating area of research that is become increasingly popular in health organization.  ...  Hospital Infection Control: A system for inspection is constructed using data mining techniques to discover unknown or irregular patterns in the infection control data [93] .  ... 
doi:10.14257/ijbsbt.2013.5.5.25 fatcat:upovsvolkrhpzbyvhg6rifd6yy

Machine learning and radiology

Shijun Wang, Ronald M. Summers
2012 Medical Image Analysis  
In many applications, the performance of machine learning-based automatic detection and diagnosis systems has shown to be comparable to that of a well-trained and experienced radiologist.  ...  Machine learning identifies complex patterns automatically and helps radiologists make intelligent decisions on radiology data such as conventional radiographs, CT, MRI, and PET images and radiology reports  ...  Acknowledgments We thank Andrew Dwyer, MD, for critical review of the manuscript. This manuscript was support by the Intramural Research Program of the National Institutes of Health Clinical Center.  ... 
doi:10.1016/j.media.2012.02.005 pmid:22465077 pmcid:PMC3372692 fatcat:4ynexgzdhrev7dfqapmjpxexuu

Unusual Event Detection via Multi-camera Video Mining

Hanning Zhou, D. Kimber
2006 18th International Conference on Pattern Recognition (ICPR'06)  
This paper describes a framework for detecting unusual events in surveillance videos.  ...  We use two-stage training to bootstrap a set of usual events, and train a CHMM over the set.  ...  In particular, we use the junction tree implementation for 2 time-slice temporal Bayesian Networks [16] . For learning the model parameters, we use the Expectation Maximization [1] algorithm.  ... 
doi:10.1109/icpr.2006.1149 dblp:conf/icpr/ZhouK06 fatcat:x5jfkoabhva6jcorhffflr4iqy

Identifying Surprising Events in Videos Using Bayesian Topic Models [chapter]

Avishai Hendel, Daphna Weinshall, Shmuel Peleg
2011 Lecture Notes in Computer Science  
Automatic processing of video data is essential in order to allow efficient access to large amounts of video content, a crucial point in such applications as video mining and surveillance.  ...  We tested our algorithm on a real dataset of video data, taken by a camera observing an urban street intersection.  ...  Other researchers use Bayesian topic models as a basis for the representation of the environment and for the application of inference algorithms.  ... 
doi:10.1007/978-3-642-19318-7_35 fatcat:ykplmgveyjcvbcx7immasbc5ay
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